Targetlynx application manager for masslynx 4

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TargetLynx application manager is a software tool for MassLynx 4.1 that provides data processing and analysis functionality. It supports the management and processing of quantitative analytical data.

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Lab products found in correlation

Precellys 24, by Bertin Technologies (1 mentions) Precellys 24 homogenizer, by Bertin Technologies (1 mentions)

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MassLynx 4.1 software (589 citations) MassLynx (183 citations) TargetLynx (72 citations) TargetLynx application manager (18 citations) TargetLynx 4.1 software (4 citations)

19 protocols using targetlynx application manager for masslynx 4

Plasma Metabolomic Profiling Using UHPLC-MS

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Plasma samples were analyzed using ultra-high-performance liquid chromatography–mass spectrometry (UHPLC-MS) to elucidate metabolomic profiles. Metabolite extraction process, chromatographic separation, and mass spectrometric detection conditions for each platform follow the procedure described by Barr et al.^{21 (link)} Quality control procedures were used to ensure high-quality data for analyses. This study used 3 UHPLC-MS platforms to cover a wide range of metabolites in the plasma sample—broadly characterized into (1) fatty acids, bile acids, steroids, and lysoglycerophospholipids; (2) glycolipids, glycerophospholipids, sterol lipids, and sphingolipids; and (3) amino acids (AAs).
Data were preprocessed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, MA).^{14 (link)} Intrabatch and interbatch normalization was performed by inclusion of multiple internal standards and pool calibration response correction, following the procedure described by Martinez-Arranz et al.^{22 (link)}

Mowry C.J., Alonso C., Iruarrizaga-Lejarreta M., Ortiz P., Levitsky J, & Rinella M. (2021). Utility of Metabolomic Biomarkers to Identify Nonalcoholic Fatty Liver Disease in Liver Transplant Recipients. Transplantation Direct, 7(12), e784.

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Quantitative Metabolite Profiling with LC-MS

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All data were processed using TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Mildfor, CT, USA). A set of predefined retention time–mass-to-charge ratio pairs, RT-m/z, corresponding to metabolites included in the analysis, and identified based on an in-house library with a mass tolerance window of 0.05 Da, were fed into the program. Associated extracted ion chromatograms (EICs; mass tolerance window = 0.05 Da) were then peak-detected and noise-reduced in both the LC and MS domain such that only true metabolite-related features were processed by the software. A list of chromatographic peak areas was then generated for each sample injection.
For identified metabolites, representative MS detection response curves were generated using an internal standard for each chemical class included in the analysis. By assuming similar detector response levels for all metabolites belonging to a given chemical class represented by a single standard compound, a linear detection range was defined for each identified metabolite. Maximum values were defined as those at which the detector response became non-linear with respect to the concentration of the representative internal standard. Variables not considered for further analysis, where more than 30% of data points were found outside their corresponding linear detection range, were removed.

Albóniga O.E., Cubiella J., Bujanda L., Blanco M.E., Lanza B., Alonso C., Nafría B, & Falcón-Pérez J.M. (2023). A Novel Approach on the Use of Samples from Faecal Occult Blood Screening Kits for Metabolomics Analysis: Application in Colorectal Cancer Population. Metabolites, 13(3), 321.

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Metabolite Quantification and Normalization

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Raw data were processed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, USA). A set of predefined retention time, mass-to-charge ratio pairs, Rt-m/z, corresponding to metabolites included in the analysis are considered. Associated extracted ion chromatograms (mass tolerance window = 0.05 Da) are then peak-detected and noise-reduced in both the LC and MS domains. A list of chromatographic peak areas is then generated for each sample injection. Normalization factors were calculated for each metabolite by dividing their intensities in each sample by the recorded intensity of an appropriate internal standard in that same sample, following the procedure described by Martinez-Arranz et al. [20] . Following normalization, sample injection data were returned for manual inspection of the automated integration performed by the TargetLynx software.

Kaoutari A.E., Fraunhoffer N.A., Hoare O., Teyssedou C., Soubeyran P., Gayet O., Roques J., Lomberk G., Urrutia R., Dusetti N, & Iovanna J. (2021). Metabolomic profiling of pancreatic adenocarcinoma reveals key features driving clinical outcome and drug resistance. EBioMedicine, 66, 103332.

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Glycerolipid Profiling in NAFLD

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Plasma samples were collected under fasted conditions: at baseline, day 14 pre-dose and day 14, 2 h post dose in NAFLD patients. Samples were immediately frozen and later analyzed by ultraperformance liquid chromatography coupled to mass spectrometry (UHPLC-MS). The analysis focused on determination of glycerolipids (15 different diacylglycerols and 94 different triacylglycerols). Briefly, proteins were precipitated by adding chloroform:methanol (2:1) containing internal standards. Homogenization of the resulting mixture was performed using a Precellys 24 homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France) at 6500 rpm for 45 s x 1 round. Homogenized samples were incubated at −20°C for 1 h and after vortexing, a 500 μL aliquot was collected. The supernatants were mixed with ammonium hydroxide in H₂O (pH 9) and incubated for 1 h at −20°C. After centrifugation, the organic phase was collected and dried under vacuum. Dried extracts were reconstituted in acetonitrile / isopropanol (1:1) for LC-MS analysis using standard methods⁶⁴ (link). All data were processed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, USA). Intra- and inter-batch normalization was performed by inclusion of multiple internal standards for each metabolite and pool calibration response correction, as previously described ⁶⁵ (link).

Fouqueray P., Bolze S., Dubourg J., Hallakou-Bozec S., Theurey P., Grouin J.M., Chevalier C., Gluais-Dagorn P., Moller D.E, & Cusi K. (2021). Pharmacodynamic effects of direct AMP kinase activation in humans with insulin resistance and non-alcoholic fatty liver disease: A phase 1b study. Cell Reports Medicine, 2(12), 100474.

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Targeted UHPLC-MS Metabolomics Workflow

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All data were processed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, USA). A set of predefined retention time, mass-to-charge ratio pairs, Rt-m/z, corresponding to metabolites included in the analysis are fed into the programme. Associated extracted ion chromatograms (mass tolerance window = 0.05 Da) are then peak-detected and noise-reduced in both the UHPLC and MS domains such that only true metabolite related features are processed by the software. A list of chromatographic peak areas is then generated for each sample injection.
Data normalisation was based on multiple internal standards and pool calibration samples approach as described by [21]. The LC-MS features were identified prior to the analysis, either by comparison of their accurate mass spectra and chromatographic Rt with those of available reference standards or, where these were not available, by accurate mass MS/MS fragment ion analysis. Lipid nomenclature and classification follows the LIPID MAPS convention, www. lipidmaps.org.

Royo F., Gil-Carton D., Gonzalez E., Mleczko J., Palomo L., Perez-Cormenzana M., Mayo R., Alonso C, & Falcon-Perez J.M. (2019). Differences in the metabolite composition and mechanical properties of extracellular vesicles secreted by hepatic cellular models. Journal of Extracellular Vesicles, 8(1), 1575678.

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Metabolomics Data Pre-processing Protocol

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Data were pre-processed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, CT, USA). Metabolites were identified prior to the analysis. Peak detection, noise reduction and data normalization were performed as previously described [106 (link)].

Urman J.M., Herranz J.M., Uriarte I., Rullán M., Oyón D., González B., Fernandez-Urién I., Carrascosa J., Bolado F., Zabalza L., Arechederra M., Alvarez-Sola G., Colyn L., Latasa M.U., Puchades-Carrasco L., Pineda-Lucena A., Iraburu M.J., Iruarrizaga-Lejarreta M., Alonso C., Sangro B., Purroy A., Gil I., Carmona L., Cubero F.J., Martínez-Chantar M.L., Banales J.M., Romero M.R., Macias R.I., Monte M.J., Marín J.J., Vila J.J., Corrales F.J., Berasain C., Fernández-Barrena M.G, & Avila M.A. (2020). Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach. Cancers, 12(6), 1644.

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Metabolite Profiling in CSF Samples

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TargetLynx application manager for MassLynx 4.1 software (Waters Corp. Milford, MA, USA) was used for data processing. A set of parameters associated to metabolites included in the analysis (Rt m/z, mass-to-charge ratio pairs, retention time) were incorporated into the program. Using a mass tolerance window of 0.05 Da and after peak detection and noise reduction (at LC and MS levels), only true metabolite related features were processed by the software. For each sample injection, a list of chromatographic peak areas was generated. Data normalization was performed following the procedure described by Barr et al. [23 (link)], where the ion intensity corresponding to each peak present in each CSF sample was normalized in respect to the sum of peak intensities in each CSF sample. There were no significant differences (t-test = 0.1031) between the total intensities used for normalization of the sample groups compared in the study.

Fernández-Irigoyen J., Cartas-Cejudo P., Iruarrizaga-Lejarreta M, & Santamaría E. (2021). Alteration in the Cerebrospinal Fluid Lipidome in Parkinson’s Disease: A Post-Mortem Pilot Study. Biomedicines, 9(5), 491.

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Liver Metabolic Profiling by UHPLC-MS

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A semiquantitative ultra-high performance liquid chromatography (UHPLC)-time of flight-MS based platform was used for the determination of the liver metabolic profiles[21 (link),22 (link)]. Briefly, methanol was added to the liver tissue (15 mg) for protein precipitation. The methanol was spiked with metabolites not detected in unspiked cell extracts that were used as internal standards. After protein precipitation, samples were homogenized (6500 rpm for 23 s × 1 round) using a Precellys 24 homogenizer (Bertin Technologies, Montigny-le-Bretonneux, France), and then centrifuged at 18000 × g for 10 min at 4 ºC. An aliquot of 500 µL was collected and mixed with chloroform. After 10 min of agitation, a second centrifugation of the samples was performed (18000 × g for 15 min at 4 ºC). Supernatants were dried under vacuum and then, reconstituted in water. After a third centrifugation (18000 × g for 15 min at 4 ºC), the extracts were transferred to plates for UHPLC-MS analysis. Metabolic features were identified prior to the analysis. LC-MS data pre-processing was performed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp., Milford, United States). Peak detection, noise reduction, and data normalization steps followed the procedures previously described[23 (link)].

Fernández-Ramos D., Lopitz-Otsoa F., Delacruz-Villar L., Bilbao J., Pagano M., Mosca L., Bizkarguenaga M., Serrano-Macia M., Azkargorta M., Iruarrizaga-Lejarreta M., Sot J., Tsvirkun D., van Liempd S.M., Goni F.M., Alonso C., Martínez-Chantar M.L., Elortza F., Hayardeny L., Lu S.C, & Mato J.M. (2020). Arachidyl amido cholanoic acid improves liver glucose and lipid homeostasis in nonalcoholic steatohepatitis via AMPK and mTOR regulation. World Journal of Gastroenterology, 26(34), 5101-5117.

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Lipidomic Profiling of the Stratum Corneum

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Lipidomics analyses were performed by OWL Metabolomics (Derio, Spain). Two ultra-high performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-ToF-MS)-based platforms were used for optimal profiling of the SC lipidome: Platform 1 was used to analyze fatty acids (FA) while glycerolipids, cholesteryl esters, and sphingolipids where analyzed in Platform 2 as previously described [55 (link)] (see Additional file 1).
For protein quantification, the Squamescan 850A (Heiland Electronic, Wetzlar, Germany) was used to determine the amount of SC removed to obtain a good indication of the depth of each tape strip taken, measuring the protein content.
All data were processed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp.) as previously described by Martínez-Arranz et al. [56 ]. The peak detection process included 139 LC–MS features.
Normalization factors were calculated following the procedure described [56 ]. Further normalization procedure was applied by dividing every sample by its protein content, as part of the biological normalization.

Marsella R., Segarra S., Ahrens K., Alonso C, & Ferrer L. (2020). Topical treatment with SPHINGOLIPIDS and GLYCOSAMINOGLYCANS for canine atopic dermatitis. BMC Veterinary Research, 16, 92.

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Metabolite Identification and Quantification Workflow

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Following metabolite identification, all data were processed using the TargetLynx application manager for MassLynx 4.1 software (Waters Corp.). A set of predefined retention time mass-to-charge ratio pairs corresponding to metabolites included in the analysis was fed into the program. Associated extracted ion chromatograms (mass tolerance window = 0.05 Da) were then peak-detected and noise-reduced in both the LC and MS domains such that only true metabolite-related features were processed by the software. A list of chromatographic peak areas was then generated for each sample injection. An approximated linear detection range was defined for each identified metabolite, assuming similar detector response levels for all metabolites belonging to a given chemical class represented by a single standard compound (35 (link)). Metabolites for which more than 30% of data points were found outside their corresponding linear detection range were excluded from statistical analyses.
Normalization factors were calculated for each metabolite by dividing their intensities in each sample by the recorded intensity of an appropriate internal standard in that same sample, following the procedure described before (36 (link)).

Adam R.C., Mintah I.J., Alexa-Braun C.A., Shihanian L.M., Lee J.S., Banerjee P., Hamon S.C., Kim H.I., Cohen J.C., Hobbs H.H., Van Hout C., Gromada J., Murphy A.J., Yancopoulos G.D., Sleeman M.W, & Gusarova V. (2020). Angiopoietin-like protein 3 governs LDL-cholesterol levels through endothelial lipase-dependent VLDL clearance. Journal of Lipid Research, 61(9), 1271-1286.

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